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Continental-scale prediction of live fuel moisture content using soil moisture information
Title | Continental-scale prediction of live fuel moisture content using soil moisture information |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Kumar, V, Dharssi, I, Yebra, M, Fox-Hughes, P |
Journal | Agricultural and Forest Meteorology |
Volume | 307 |
Date Published | 06/2021 |
Keywords | AFMS, Fire, JASMIN, JULES, live fuel moisture, Soil moisture |
Abstract | Live fuel moisture content (LFMC) is a key factor that determines the flammability of vegetation in ecosystems. Soil moisture (SM) is one of the variables that is known to influence plant water use. The present study analyses the LFMC-SM relationship over Australia using gridded, remote sensing-based LFMC and land surface model-based SM products. A lag-correlation analysis conducted over 60 selected sites shows that the strength of the relationship between LFMC and SM varies from site to site and, in general, is moderately strong (median lag-correlation of ~0.5). However, the strength of the relationship changes with vegetation type and also with soil profile depth. At all the sites, SM is found to be a leading indicator of LFMC. The lag also varies with the location and is found to range from days to months. Based on the location-based correlation analysis, we identify the 0-35 cm SM profile (SM0-35cm) to be the best predictor of LFMC. We developed a simple model to predict daily LFMC, where it is hypothesised that daily variations in LFMC from its annual cycle can be predicted using daily deviations from the annual cycle in SM0-35cm. The annual cycles of LFMC and SM0-35cm are modelled using Fourier cosine series. The averaged (over 60 sites) correlation obtained for the validation period is 0.74 when a time-lag of 14 days is assumed at all locations. When the model is applied nationally at a 5 km grid, the normalised root mean squared error for the validation period is found to be less than 25% in general. The results from the present study highlight a modelling strategy that can be used to address a critical gap in the forecast of spatially and temporally continuous LFMC at regional scales in advance for operational fire management applications. |
URL | https://www.sciencedirect.com/science/article/abs/pii/S0168192321001866 |
DOI | 10.1016/j.agrformet.2021.108503 |
Refereed Designation | Refereed |
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